1. Field of Invention
The present invention relates generally to computing systems for use in financial data analysis. More particularly, the present invention relates to methods and apparatus for efficiently enabling financial data analysis to occur in a distributed computing environment.
2. Description of the Related Art
As the use of bankcards is becoming more prevalent, issuers of bankcards are finding that their credit and fraud charge-offs, including bankruptcy losses, are increasing. When a bankcard account holder is forced to default on payments for transactions, e.g., financial transactions, performed using his or her bankcard, it is the issuers of the bankcards who are most often forced to absorb the associated losses. Hence, to protect themselves financially, issuers of bankcards are developing so-called “risk prediction” models which they use to assess risks, e.g., bankruptcy risk, fraud risk and non-bankruptcy risk, associated with a bankcard account holder. Risk prediction models for the detection of fraud are typically based upon the analysis of patterns exhibited in series of transactions performed by the bankcard holder in a single account.
Models for evaluating bankruptcy and credit risks are typically based on historical payment data and account performance data. Generally, risk prediction models for the evaluation of bankruptcy and credit risk use historical account performance data associated with a bankcard account or, more generally, the holder of a bankcard account, to identify a pattern of payment and to correlate the pattern of payment to known patterns of payment. In other words, the payment pattern of the account holder is compared against payment patterns which are considered as being indicative of a relatively high risk of future financial problems, as for example bankruptcy or credit loss.
With respect to fraud detection systems transaction data (data in the format of a string of data containing a series of different data fields), typically is not used directly by the fraud detection models. In general, the transaction data, which includes such data as an account number, a transaction amount, a transaction time, and a merchant zip code, as well as various other data, must be transformed into characteristic variables which may be used as direct inputs to the risk prediction models. These characteristic variables include, for example, a variable which holds the risk associated with a transaction occurring in a particular geographic area, a time-weighted sum of the total number of consummated financial purchases, and a running sum of the total amount of consummated purchases.
Typically, characteristic variables are generated from transaction data at a central server. In other words, transaction data is provided to a central server from a client, e.g., a payment gateway or a customer computer, and the central server processes, i.e., scores, the transaction data. To provide transaction data to a central server, the transaction data is typically sent from a client over a network connection. Such data transmission generally involves sending private information, e.g., credit account numbers, over the network connection. As the private information is being transmitted it may be accessed by virtually any individual who has access to the network connection. In addition, even if present at a central location, the private information might be viewed by a programmer or other data processor who is scoring the information. When the private information is accessed by an individual who wishes to use the information fraudulently, the integrity of the account number associated with the information may be compromised (among other information).
Further, additional information which may be useful in assessing risk, or generating a score, is often not transmitted from a client to a central server for processing. That is, some “at-source” data is often not provided to and, hence, unavailable to, a transaction server. As such, information that is potentially relevant to assessing risk may not be included in a risk assessment. For example, during an Internet transaction, information related to a web browser, the TCP/IP address, etc., is often not transmitted to a central server. Other types of “at source” data might not be transmitted to a central server.
Transaction data could be processed at distributed locations, but this presents problems relating to algorithms for scoring and how to keep the data secret at distributed locations. Therefore, what is needed is a method and an apparatus which is secure and enables substantially all at-source data to be used in a scoring process. In other words, what is desired is a secure, distributed system which enables transactions to be scored.